Energy theft, characterized by manipulating energy consumption readings to reduce payments, poses a dual threat-causing financial losses for grid operators and undermining the performance of smart grids. Effective Energy Theft Detection (ETD) methods become crucial in mitigating these risks by identifying such fraudulent activities in their early stages. However, the majority of current ETD methods rely on supervised learning, which is hindered by the difficulty of labelling data and the risk of overfitting known attacks. To address these challenges, several unsupervised ETD methods have been proposed, focusing on learning the normal patterns from honest users, specifically the reconstruction of input. However, our investigation reveals a limitation in current unsupervised ETD methods, as they can only detect anomalous behaviours in users exhibiting regular patterns. Users with high-variance behaviours pose a challenge to these methods. In response, this paper introduces a Denoising Diffusion Probabilistic Model (DDPM)-based ETD approach. This innovative approach demonstrates impressive ETD performance on high-variance smart grid data by incorporating additional attributes correlated with energy consumption. The proposed methods improve the average ETD performance on high-variance smart grid data from below 0.5 to over 0.9 w.r.t. AUC. On the other hand, our experimental findings indicate that while the state-of-the-art ETD methods based on reconstruction error can identify ETD attacks for the majority of users, they prove ineffective in detecting attacks for certain users. To address this, we propose a novel ensemble approach that considers both reconstruction error and forecasting error, enhancing the robustness of the ETD methodology. The proposed ensemble method improves the average ETD performance on the stealthiest attacks from nearly 0 to 0.5 w.r.t. 5%-TPR.

翻译：窃电行为通过篡改用电量读数以减少电费支出，对电网运营商造成经济损失并削弱智能电网性能，构成双重威胁。有效的窃电检测方法对于通过早期识别此类欺诈行为以降低相关风险至关重要。然而，现有窃电检测方法大多依赖监督学习，受限于数据标记困难及对已知攻击的过拟合风险。为应对这些挑战，研究者提出了多种无监督窃电检测方法，重点从合法用户中学习正常模式（尤其是输入重构）。但我们的研究发现，当前无监督方法存在局限性——仅能检测具有规律性用电模式用户的异常行为，而对高变异行为用户难以奏效。为此，本文提出一种基于去噪扩散概率模型的窃电检测方法。该创新方法通过整合与能耗相关的附加属性，在高变异智能电网数据上展现出卓越的检测性能，将AUC指标从0.5以下提升至0.9以上。另一方面，实验结果表明：尽管当前基于重构误差的最优窃电检测方法可识别大多数用户的攻击行为，但对特定用户群体效果欠佳。针对该问题，我们提出一种融合重构误差与预测误差的新型集成方法，显著增强了窃电检测方法的鲁棒性。所提集成方法在最隐蔽攻击场景下，将5%真阳性率指标从接近0提升至0.5。